Ready to Elevate Your Product Experience ?
Whether you're launching a new product or refining an existing digital product, I'm here to help. Share the details about your project and I'll get back to you to work something out.
Healthcare Staffing · B2B
The AI does the reading. Recruiters do the deciding. Most of this project was working out exactly where that line sits.
Role
Product Designer
PLatform
B2B Enterprise Web App
Status
Shipped (2025)
Industry
Healthcare Staffing
FlexCare is an AI-powered recruitment platform built for healthcare staffing agencies to streamline the hiring of travel nurses. By automating candidate qualification, credential verification, and intelligent job matching, it enables recruiters to focus on faster, more confident hiring decisions while retaining full control over the recruitment process.
Recruiters spent hours manually reviewing applications, validating licenses, and comparing candidates across multiple systems. This repetitive process slowed hiring and made it difficult to scale recruitment.
Designed an AI-assisted recruitment workflow that automates candidate intake, credential validation, intelligent job matching, and recruiter insights while keeping every hiring decision human-led.
A workflow built to collapse manual verification into a single review step, replace one opaque score with an inspectable one, and give recruiters a ranked shortlist instead of a spreadsheet.





Healthcare staffing is a race against time. Recruiters often manage hundreds of travel nurse applications while juggling license verification, credential checks, job matching, and candidate communication. Most of this work is repetitive and manual, leaving less time for evaluating the right candidates.
Process
The project ran over two weeks with the client's Product Manager as the only stakeholder. He shared the requirements, explained the healthcare domain, and validated decisions through the existing PRD.
Without access to recruiters, I researched AI patterns across recruitment platforms outside healthcare to understand what had become standard and what still needed explanation. That informed features like confidence scoring and explainability, while keeping the solution aligned with the existing product architecture.
One stakeholder throughout the project.
No direct access to recruiters or end users.
Cross-industry pattern research instead of formal user research.
Product decisions validated through the Product Manager.
Instead of replacing recruiters, AI supports them throughout the recruitment journey by handling repetitive tasks and surfacing insights before human review.
Extracts and structures candidate information
Generates AI-powered candidate summaries
Provides transparent scoring to support recruiter decisions
Validates licenses and credentials
Recommends best-fit candidates for open roles
Reduce repetitive analysis while preserving recruiter accountability.
Automation trade-offs
The question was never whether the AI should do a thing. It was how much of it, and what the recruiter got back in exchange. I made those calls myself. The only thing I took to the PM was whether something could be built inside the architecture they already had.

How good is this nurse?
One score would have been easier to build and easier to read. We kept them separate because recruiters answer different questions before every placement.
Candidate Quality. How good is this nurse overall? Reliability, responsiveness, credential readiness and flexibility, each carrying its own weight.
Match. How well does this nurse fit this particular job? Location, specialty, pay, shift and availability.
Job Quality. Is this job worth the week? Fill history, market pay and facility reliability predict how hard the role will be to fill.
These scores rarely tell the same story. A great nurse can be the wrong fit for a job. A perfect match can still be tied to a role that's unlikely to fill. Keeping them separate helps recruiters decide who to call and which jobs deserve their time.



The AI does the reading at the front and the ranking in the middle. Every point where something actually happens to a nurse is a recruiter.
Recruiters start with a list of open travel nursing jobs showing priority, pay, duration, certifications, and facility details. For each job, AI continuously evaluates the candidate pool and identifies the strongest matches based on specialty, location, pay expectations, shift preference, licenses, and availability.
Selecting View Matches opens a ranked list of candidates for that specific job, along with the key factors behind each recommendation, helping recruiters compare candidates and submit the best fit faster.
Every candidate application enters the AI Intake Queue as soon as it is submitted. AI automatically extracts information from the resume, structures the candidate profile, identifies specialties and certifications, and assigns AI and Qualification scores based on the completeness and relevance of the profile.
Recruiters can monitor review progress, filter candidates by key attributes, inspect individual profiles, and approve qualified candidates directly from the queue. The same information is available in both List and Grid views, allowing recruiters to work in the layout they prefer.


Rather than reviewing resumes, certifications, and online profiles separately, recruiters can validate everything from one screen. AI summarizes the candidate's background, verifies available information, flags missing requirements, and organizes completed and pending validations.
Once the profile is fully reviewed, recruiters can confidently add the candidate to the Matching Pool, creating a shortlist of qualified candidates ready to be matched with suitable travel nursing jobs.

Human-in-the-Loop AI
AI assists, recruiters decide.
Explainable AI
Recommendations are supported with transparent reasoning.
Reduce Repetitive Work
Automate operational tasks, not decision-making.
Decision-First Design
Every screen helps recruiters answer the next question quickly.
I moved to another project shortly after handoff, so I don't have adoption metrics or placement data.
What I do have is the client's feedback. After reviewing the platform, the FlexCare client said,
This is what I wanted.
The Product Manager called me afterwards to share how happy the client was with the design and the overall workflow.
Thank you!
Whether you're launching a new product or refining an existing digital product, I'm here to help. Share the details about your project and I'll get back to you to work something out.